Upload cafe_data_cleaning.ipynb with huggingface_hub

cafe_data_cleaning.ipynb

@@ -0,0 +1,2128 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = pd.read_csv(\"dirty_cafe_sales.csv\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Transaction ID</th>\n",
+       "      <th>Item</th>\n",
+       "      <th>Quantity</th>\n",
+       "      <th>Price Per Unit</th>\n",
+       "      <th>Total Spent</th>\n",
+       "      <th>Payment Method</th>\n",
+       "      <th>Location</th>\n",
+       "      <th>Transaction Date</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>TXN_1961373</td>\n",
+       "      <td>Coffee</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>Credit Card</td>\n",
+       "      <td>Takeaway</td>\n",
+       "      <td>2023-09-08</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>TXN_4977031</td>\n",
+       "      <td>Cake</td>\n",
+       "      <td>4</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>12.0</td>\n",
+       "      <td>Cash</td>\n",
+       "      <td>In-store</td>\n",
+       "      <td>2023-05-16</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>TXN_4271903</td>\n",
+       "      <td>Cookie</td>\n",
+       "      <td>4</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>ERROR</td>\n",
+       "      <td>Credit Card</td>\n",
+       "      <td>In-store</td>\n",
+       "      <td>2023-07-19</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>TXN_7034554</td>\n",
+       "      <td>Salad</td>\n",
+       "      <td>2</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>UNKNOWN</td>\n",
+       "      <td>UNKNOWN</td>\n",
+       "      <td>2023-04-27</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>TXN_3160411</td>\n",
+       "      <td>Coffee</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>Digital Wallet</td>\n",
+       "      <td>In-store</td>\n",
+       "      <td>2023-06-11</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  Transaction ID    Item Quantity Price Per Unit Total Spent  Payment Method  \\\n",
+       "0    TXN_1961373  Coffee        2            2.0         4.0     Credit Card   \n",
+       "1    TXN_4977031    Cake        4            3.0        12.0            Cash   \n",
+       "2    TXN_4271903  Cookie        4            1.0       ERROR     Credit Card   \n",
+       "3    TXN_7034554   Salad        2            5.0        10.0         UNKNOWN   \n",
+       "4    TXN_3160411  Coffee        2            2.0         4.0  Digital Wallet   \n",
+       "\n",
+       "   Location Transaction Date  \n",
+       "0  Takeaway       2023-09-08  \n",
+       "1  In-store       2023-05-16  \n",
+       "2  In-store       2023-07-19  \n",
+       "3   UNKNOWN       2023-04-27  \n",
+       "4  In-store       2023-06-11  "
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(10000, 8)"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.DataFrame'>\n",
+      "RangeIndex: 10000 entries, 0 to 9999\n",
+      "Data columns (total 8 columns):\n",
+      " #   Column            Non-Null Count  Dtype\n",
+      "---  ------            --------------  -----\n",
+      " 0   Transaction ID    10000 non-null  str  \n",
+      " 1   Item              9667 non-null   str  \n",
+      " 2   Quantity          9862 non-null   str  \n",
+      " 3   Price Per Unit    9821 non-null   str  \n",
+      " 4   Total Spent       9827 non-null   str  \n",
+      " 5   Payment Method    7421 non-null   str  \n",
+      " 6   Location          6735 non-null   str  \n",
+      " 7   Transaction Date  9841 non-null   str  \n",
+      "dtypes: str(8)\n",
+      "memory usage: 625.1 KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Transaction ID         0\n",
+       "Item                 333\n",
+       "Quantity             138\n",
+       "Price Per Unit       179\n",
+       "Total Spent          173\n",
+       "Payment Method      2579\n",
+       "Location            3265\n",
+       "Transaction Date     159\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "--- Transaction ID ---\n",
+      "<StringArray>\n",
+      "['TXN_1961373', 'TXN_4977031', 'TXN_4271903', 'TXN_7034554', 'TXN_3160411',\n",
+      " 'TXN_2602893', 'TXN_4433211', 'TXN_6699534', 'TXN_4717867', 'TXN_2064365',\n",
+      " 'TXN_2548360', 'TXN_3051279', 'TXN_7619095', 'TXN_9437049', 'TXN_8915701',\n",
+      " 'TXN_2847255', 'TXN_3765707', 'TXN_6769710', 'TXN_8876618', 'TXN_3709394']\n",
+      "Length: 20, dtype: str\n",
+      "\n",
+      "--- Item ---\n",
+      "<StringArray>\n",
+      "[  'Coffee',     'Cake',   'Cookie',    'Salad', 'Smoothie',  'UNKNOWN',\n",
+      " 'Sandwich',        nan,    'ERROR',    'Juice',      'Tea']\n",
+      "Length: 11, dtype: str\n",
+      "\n",
+      "--- Quantity ---\n",
+      "<StringArray>\n",
+      "['2', '4', '5', '3', '1', 'ERROR', 'UNKNOWN', nan]\n",
+      "Length: 8, dtype: str\n",
+      "\n",
+      "--- Price Per Unit ---\n",
+      "<StringArray>\n",
+      "['2.0', '3.0', '1.0', '5.0', '4.0', '1.5', nan, 'ERROR', 'UNKNOWN']\n",
+      "Length: 9, dtype: str\n",
+      "\n",
+      "--- Total Spent ---\n",
+      "<StringArray>\n",
+      "[    '4.0',    '12.0',   'ERROR',    '10.0',    '20.0',     '9.0',    '16.0',\n",
+      "    '15.0',    '25.0',     '8.0',     '5.0',     '3.0',     '6.0',       nan,\n",
+      " 'UNKNOWN',     '2.0',     '1.0',     '7.5',     '4.5',     '1.5']\n",
+      "Length: 20, dtype: str\n",
+      "\n",
+      "--- Payment Method ---\n",
+      "<StringArray>\n",
+      "['Credit Card', 'Cash', 'UNKNOWN', 'Digital Wallet', 'ERROR', nan]\n",
+      "Length: 6, dtype: str\n",
+      "\n",
+      "--- Location ---\n",
+      "<StringArray>\n",
+      "['Takeaway', 'In-store', 'UNKNOWN', nan, 'ERROR']\n",
+      "Length: 5, dtype: str\n",
+      "\n",
+      "--- Transaction Date ---\n",
+      "<StringArray>\n",
+      "['2023-09-08', '2023-05-16', '2023-07-19', '2023-04-27', '2023-06-11',\n",
+      " '2023-03-31', '2023-10-06', '2023-10-28', '2023-07-28', '2023-12-31',\n",
+      " '2023-11-07',      'ERROR', '2023-05-03', '2023-06-01', '2023-03-21',\n",
+      " '2023-11-15', '2023-06-10', '2023-02-24', '2023-03-25', '2023-01-15']\n",
+      "Length: 20, dtype: str\n"
+     ]
+    }
+   ],
+   "source": [
+    "for col in df.columns:\n",
+    "    print(f\"\\n--- {col} ---\")\n",
+    "    print(df[col].unique()[:20])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = df.replace([\"ERROR\", \"UNKNOWN\"], np.nan)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Transaction ID         0\n",
+       "Item                 969\n",
+       "Quantity             479\n",
+       "Price Per Unit       533\n",
+       "Total Spent          502\n",
+       "Payment Method      3178\n",
+       "Location            3961\n",
+       "Transaction Date     460\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Transaction ID</th>\n",
+       "      <th>Item</th>\n",
+       "      <th>Quantity</th>\n",
+       "      <th>Price Per Unit</th>\n",
+       "      <th>Total Spent</th>\n",
+       "      <th>Payment Method</th>\n",
+       "      <th>Location</th>\n",
+       "      <th>Transaction Date</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>TXN_1961373</td>\n",
+       "      <td>Coffee</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>Credit Card</td>\n",
+       "      <td>Takeaway</td>\n",
+       "      <td>2023-09-08</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>TXN_4977031</td>\n",
+       "      <td>Cake</td>\n",
+       "      <td>4</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>12.0</td>\n",
+       "      <td>Cash</td>\n",
+       "      <td>In-store</td>\n",
+       "      <td>2023-05-16</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>TXN_4271903</td>\n",
+       "      <td>Cookie</td>\n",
+       "      <td>4</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Credit Card</td>\n",
+       "      <td>In-store</td>\n",
+       "      <td>2023-07-19</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>TXN_7034554</td>\n",
+       "      <td>Salad</td>\n",
+       "      <td>2</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2023-04-27</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>TXN_3160411</td>\n",
+       "      <td>Coffee</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>Digital Wallet</td>\n",
+       "      <td>In-store</td>\n",
+       "      <td>2023-06-11</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  Transaction ID    Item Quantity Price Per Unit Total Spent  Payment Method  \\\n",
+       "0    TXN_1961373  Coffee        2            2.0         4.0     Credit Card   \n",
+       "1    TXN_4977031    Cake        4            3.0        12.0            Cash   \n",
+       "2    TXN_4271903  Cookie        4            1.0         NaN     Credit Card   \n",
+       "3    TXN_7034554   Salad        2            5.0        10.0             NaN   \n",
+       "4    TXN_3160411  Coffee        2            2.0         4.0  Digital Wallet   \n",
+       "\n",
+       "   Location Transaction Date  \n",
+       "0  Takeaway       2023-09-08  \n",
+       "1  In-store       2023-05-16  \n",
+       "2  In-store       2023-07-19  \n",
+       "3       NaN       2023-04-27  \n",
+       "4  In-store       2023-06-11  "
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Missing Count</th>\n",
+       "      <th>Missing Percentage</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Transaction ID</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Item</th>\n",
+       "      <td>969</td>\n",
+       "      <td>9.69</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Quantity</th>\n",
+       "      <td>479</td>\n",
+       "      <td>4.79</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Price Per Unit</th>\n",
+       "      <td>533</td>\n",
+       "      <td>5.33</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Total Spent</th>\n",
+       "      <td>502</td>\n",
+       "      <td>5.02</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Payment Method</th>\n",
+       "      <td>3178</td>\n",
+       "      <td>31.78</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Location</th>\n",
+       "      <td>3961</td>\n",
+       "      <td>39.61</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Transaction Date</th>\n",
+       "      <td>460</td>\n",
+       "      <td>4.60</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                  Missing Count  Missing Percentage\n",
+       "Transaction ID                0                0.00\n",
+       "Item                        969                9.69\n",
+       "Quantity                    479                4.79\n",
+       "Price Per Unit              533                5.33\n",
+       "Total Spent                 502                5.02\n",
+       "Payment Method             3178               31.78\n",
+       "Location                   3961               39.61\n",
+       "Transaction Date            460                4.60"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "missing_summary = pd.DataFrame({\n",
+    "    \"Missing Count\": df.isnull().sum(),\n",
+    "    \"Missing Percentage\": (df.isnull().sum() / len(df) * 100).round(2)\n",
+    "})\n",
+    "\n",
+    "missing_summary"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Fill missing values in categorical columns\n",
+    "\n",
+    "# Item → fill with mode\n",
+    "df[\"Item\"] = df[\"Item\"].fillna(df[\"Item\"].mode()[0])\n",
+    "\n",
+    "# Payment Method → fill with mode\n",
+    "df[\"Payment Method\"] = df[\"Payment Method\"].fillna(\n",
+    "    df[\"Payment Method\"].mode()[0]\n",
+    ")\n",
+    "\n",
+    "# Location → fill missing values with \"Unknown\"\n",
+    "df[\"Location\"] = df[\"Location\"].fillna(\"Unknown\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Item              0\n",
+       "Payment Method    0\n",
+       "Location          0\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df[[\"Item\", \"Payment Method\", \"Location\"]].isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Location\n",
+       "Unknown     3961\n",
+       "Takeaway    3022\n",
+       "In-store    3017\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df[\"Location\"].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "numeric_columns = [\"Quantity\", \"Price Per Unit\", \"Total Spent\"]\n",
+    "\n",
+    "for col in numeric_columns:\n",
+    "    df[col] = pd.to_numeric(df[col], errors=\"coerce\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Quantity          float64\n",
+       "Price Per Unit    float64\n",
+       "Total Spent       float64\n",
+       "dtype: object"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df[numeric_columns].dtypes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Quantity          479\n",
+       "Price Per Unit    533\n",
+       "Total Spent       502\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df[numeric_columns].isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Actual Total</th>\n",
+       "      <th>Calculated Total</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>4.0</td>\n",
+       "      <td>4.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>12.0</td>\n",
+       "      <td>12.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>4.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>10.0</td>\n",
+       "      <td>10.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>4.0</td>\n",
+       "      <td>4.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>20.0</td>\n",
+       "      <td>20.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>9.0</td>\n",
+       "      <td>9.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>16.0</td>\n",
+       "      <td>16.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>15.0</td>\n",
+       "      <td>15.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>20.0</td>\n",
+       "      <td>20.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Actual Total  Calculated Total\n",
+       "0           4.0               4.0\n",
+       "1          12.0              12.0\n",
+       "2           NaN               4.0\n",
+       "3          10.0              10.0\n",
+       "4           4.0               4.0\n",
+       "5          20.0              20.0\n",
+       "6           9.0               9.0\n",
+       "7          16.0              16.0\n",
+       "8          15.0              15.0\n",
+       "9          20.0              20.0"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "calculated_total = df[\"Quantity\"] * df[\"Price Per Unit\"]\n",
+    "\n",
+    "comparison = pd.DataFrame({\n",
+    "    \"Actual Total\": df[\"Total Spent\"],\n",
+    "    \"Calculated Total\": calculated_total\n",
+    "})\n",
+    "\n",
+    "comparison.head(10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "np.int64(0)"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "mismatch = (\n",
+    "    df[\"Total Spent\"].notna()\n",
+    "    & calculated_total.notna()\n",
+    "    & (df[\"Total Spent\"] != calculated_total)\n",
+    ")\n",
+    "\n",
+    "mismatch.sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Item</th>\n",
+       "      <th>Quantity</th>\n",
+       "      <th>Price Per Unit</th>\n",
+       "      <th>Total Spent</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "Empty DataFrame\n",
+       "Columns: [Item, Quantity, Price Per Unit, Total Spent]\n",
+       "Index: []"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.loc[mismatch, [\n",
+    "    \"Item\",\n",
+    "    \"Quantity\",\n",
+    "    \"Price Per Unit\",\n",
+    "    \"Total Spent\"\n",
+    "]].head(20)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Quantity          3.0\n",
+       "Price Per Unit    3.0\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df[[\"Quantity\", \"Price Per Unit\"]].median()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df[\"Quantity\"] = df[\"Quantity\"].fillna(df[\"Quantity\"].median())\n",
+    "\n",
+    "df[\"Price Per Unit\"] = df[\"Price Per Unit\"].fillna(\n",
+    "    df[\"Price Per Unit\"].median()\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Quantity          0\n",
+       "Price Per Unit    0\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df[[\"Quantity\", \"Price Per Unit\"]].isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df[\"Total Spent\"] = df[\"Total Spent\"].fillna(\n",
+    "    df[\"Quantity\"] * df[\"Price Per Unit\"]\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "np.int64(0)"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df[\"Total Spent\"].isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df[\"Transaction Date\"] = pd.to_datetime(\n",
+    "    df[\"Transaction Date\"],\n",
+    "    errors=\"coerce\"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "dtype('<M8[us]')"
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df[\"Transaction Date\"].dtype"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "np.int64(460)"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df[\"Transaction Date\"].isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "np.int64(460)"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df[\"Transaction Date\"].isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = df.dropna(subset=[\"Transaction Date\"]).copy()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "np.int64(0)"
+      ]
+     },
+     "execution_count": 39,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df[\"Transaction Date\"].isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(9540, 8)"
+      ]
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "np.int64(0)"
+      ]
+     },
+     "execution_count": 41,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.duplicated().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "np.int64(0)"
+      ]
+     },
+     "execution_count": 42,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df[\"Transaction ID\"].duplicated().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Transaction ID</th>\n",
+       "      <th>Item</th>\n",
+       "      <th>Quantity</th>\n",
+       "      <th>Price Per Unit</th>\n",
+       "      <th>Total Spent</th>\n",
+       "      <th>Payment Method</th>\n",
+       "      <th>Location</th>\n",
+       "      <th>Transaction Date</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "Empty DataFrame\n",
+       "Columns: [Transaction ID, Item, Quantity, Price Per Unit, Total Spent, Payment Method, Location, Transaction Date]\n",
+       "Index: []"
+      ]
+     },
+     "execution_count": 43,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df[df[\"Transaction ID\"].duplicated(keep=False)].sort_values(\"Transaction ID\").head(20)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Quantity</th>\n",
+       "      <th>Price Per Unit</th>\n",
+       "      <th>Total Spent</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>9540.000000</td>\n",
+       "      <td>9540.000000</td>\n",
+       "      <td>9540.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>3.024109</td>\n",
+       "      <td>2.952673</td>\n",
+       "      <td>8.924109</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>1.384186</td>\n",
+       "      <td>1.243449</td>\n",
+       "      <td>5.997956</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>4.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>8.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>4.000000</td>\n",
+       "      <td>4.000000</td>\n",
+       "      <td>12.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>5.000000</td>\n",
+       "      <td>5.000000</td>\n",
+       "      <td>25.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          Quantity  Price Per Unit  Total Spent\n",
+       "count  9540.000000     9540.000000  9540.000000\n",
+       "mean      3.024109        2.952673     8.924109\n",
+       "std       1.384186        1.243449     5.997956\n",
+       "min       1.000000        1.000000     1.000000\n",
+       "25%       2.000000        2.000000     4.000000\n",
+       "50%       3.000000        3.000000     8.000000\n",
+       "75%       4.000000        4.000000    12.000000\n",
+       "max       5.000000        5.000000    25.000000"
+      ]
+     },
+     "execution_count": 44,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df[[\"Quantity\", \"Price Per Unit\", \"Total Spent\"]].describe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Quantity:\n",
+      "Negative values: 0\n",
+      "Zero values: 0\n",
+      "\n",
+      "Price Per Unit:\n",
+      "Negative values: 0\n",
+      "Zero values: 0\n",
+      "\n",
+      "Total Spent:\n",
+      "Negative values: 0\n",
+      "Zero values: 0\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "for col in [\"Quantity\", \"Price Per Unit\", \"Total Spent\"]:\n",
+    "    print(f\"{col}:\")\n",
+    "    print(\"Negative values:\", (df[col] < 0).sum())\n",
+    "    print(\"Zero values:\", (df[col] == 0).sum())\n",
+    "    print()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Quantity:\n",
+      "[np.float64(1.0), np.float64(2.0), np.float64(3.0), np.float64(4.0), np.float64(5.0)]\n",
+      "\n",
+      "Price Per Unit:\n",
+      "[np.float64(1.0), np.float64(1.5), np.float64(2.0), np.float64(3.0), np.float64(4.0), np.float64(5.0)]\n",
+      "\n",
+      "Total Spent:\n",
+      "[np.float64(1.0), np.float64(1.5), np.float64(2.0), np.float64(3.0), np.float64(4.0), np.float64(4.5), np.float64(5.0), np.float64(6.0), np.float64(7.5), np.float64(8.0), np.float64(9.0), np.float64(10.0), np.float64(12.0), np.float64(15.0), np.float64(16.0), np.float64(20.0), np.float64(25.0)]\n"
+     ]
+    }
+   ],
+   "source": [
+    "for col in [\"Quantity\", \"Price Per Unit\", \"Total Spent\"]:\n",
+    "    print(f\"\\n{col}:\")\n",
+    "    print(sorted(df[col].dropna().unique()))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "categorical_columns = [\n",
+    "    \"Item\",\n",
+    "    \"Payment Method\",\n",
+    "    \"Location\"\n",
+    "]\n",
+    "\n",
+    "for col in categorical_columns:\n",
+    "    df[col] = df[col].str.strip()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "--- Item ---\n",
+      "<StringArray>\n",
+      "['Coffee', 'Cake', 'Cookie', 'Salad', 'Smoothie', 'Juice', 'Sandwich', 'Tea']\n",
+      "Length: 8, dtype: str\n",
+      "\n",
+      "--- Payment Method ---\n",
+      "<StringArray>\n",
+      "['Credit Card', 'Cash', 'Digital Wallet']\n",
+      "Length: 3, dtype: str\n",
+      "\n",
+      "--- Location ---\n",
+      "<StringArray>\n",
+      "['Takeaway', 'In-store', 'Unknown']\n",
+      "Length: 3, dtype: str\n"
+     ]
+    }
+   ],
+   "source": [
+    "for col in categorical_columns:\n",
+    "    print(f\"\\n--- {col} ---\")\n",
+    "    print(df[col].unique())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Item value counts:\n",
+      "Item\n",
+      "Juice       2051\n",
+      "Coffee      1123\n",
+      "Salad       1099\n",
+      "Cake        1082\n",
+      "Sandwich    1075\n",
+      "Smoothie    1048\n",
+      "Cookie      1035\n",
+      "Tea         1027\n",
+      "Name: count, dtype: int64\n",
+      "\n",
+      "Payment Method value counts:\n",
+      "Payment Method\n",
+      "Digital Wallet    5212\n",
+      "Credit Card       2170\n",
+      "Cash              2158\n",
+      "Name: count, dtype: int64\n",
+      "\n",
+      "Location value counts:\n",
+      "Location\n",
+      "Unknown     3779\n",
+      "Takeaway    2889\n",
+      "In-store    2872\n",
+      "Name: count, dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "for col in categorical_columns:\n",
+    "    print(f\"\\n{col} value counts:\")\n",
+    "    print(df[col].value_counts())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Final Dataset Quality Check"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Transaction ID      0\n",
+       "Item                0\n",
+       "Quantity            0\n",
+       "Price Per Unit      0\n",
+       "Total Spent         0\n",
+       "Payment Method      0\n",
+       "Location            0\n",
+       "Transaction Date    0\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 50,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Transaction ID                 str\n",
+       "Item                           str\n",
+       "Quantity                   float64\n",
+       "Price Per Unit             float64\n",
+       "Total Spent                float64\n",
+       "Payment Method                 str\n",
+       "Location                       str\n",
+       "Transaction Date    datetime64[us]\n",
+       "dtype: object"
+      ]
+     },
+     "execution_count": 51,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.dtypes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Duplicate rows: 0\n",
+      "Duplicate Transaction IDs: 0\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Duplicate rows:\", df.duplicated().sum())\n",
+    "print(\"Duplicate Transaction IDs:\", df[\"Transaction ID\"].duplicated().sum())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Total Spent mismatches: 696\n"
+     ]
+    }
+   ],
+   "source": [
+    "calculated_total = df[\"Quantity\"] * df[\"Price Per Unit\"]\n",
+    "\n",
+    "total_mismatch = (\n",
+    "    (df[\"Total Spent\"] - calculated_total).abs() > 0.001\n",
+    ")\n",
+    "\n",
+    "print(\"Total Spent mismatches:\", total_mismatch.sum())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df[\"Total Spent\"] = df[\"Quantity\"] * df[\"Price Per Unit\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Total Spent mismatches: 0\n"
+     ]
+    }
+   ],
+   "source": [
+    "calculated_total = df[\"Quantity\"] * df[\"Price Per Unit\"]\n",
+    "\n",
+    "total_mismatch = (\n",
+    "    (df[\"Total Spent\"] - calculated_total).abs() > 0.001\n",
+    ")\n",
+    "\n",
+    "print(\"Total Spent mismatches:\", total_mismatch.sum())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Dataset Shape: (9540, 8)\n",
+      "\n",
+      "Missing Values:\n",
+      "Transaction ID      0\n",
+      "Item                0\n",
+      "Quantity            0\n",
+      "Price Per Unit      0\n",
+      "Total Spent         0\n",
+      "Payment Method      0\n",
+      "Location            0\n",
+      "Transaction Date    0\n",
+      "dtype: int64\n",
+      "\n",
+      "Duplicate Rows: 0\n",
+      "Duplicate Transaction IDs: 0\n",
+      "\n",
+      "Data Types:\n",
+      "Transaction ID                 str\n",
+      "Item                           str\n",
+      "Quantity                   float64\n",
+      "Price Per Unit             float64\n",
+      "Total Spent                float64\n",
+      "Payment Method                 str\n",
+      "Location                       str\n",
+      "Transaction Date    datetime64[us]\n",
+      "dtype: object\n",
+      "\n",
+      "Total Spent Mismatches:\n",
+      "0\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Dataset Shape:\", df.shape)\n",
+    "print(\"\\nMissing Values:\")\n",
+    "print(df.isnull().sum())\n",
+    "\n",
+    "print(\"\\nDuplicate Rows:\", df.duplicated().sum())\n",
+    "print(\"Duplicate Transaction IDs:\", df[\"Transaction ID\"].duplicated().sum())\n",
+    "\n",
+    "print(\"\\nData Types:\")\n",
+    "print(df.dtypes)\n",
+    "\n",
+    "print(\"\\nTotal Spent Mismatches:\")\n",
+    "\n",
+    "calculated_total = df[\"Quantity\"] * df[\"Price Per Unit\"]\n",
+    "\n",
+    "total_mismatch = (\n",
+    "    (df[\"Total Spent\"] - calculated_total).abs() > 0.001\n",
+    ")\n",
+    "\n",
+    "print(total_mismatch.sum())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Transaction ID</th>\n",
+       "      <th>Item</th>\n",
+       "      <th>Quantity</th>\n",
+       "      <th>Price Per Unit</th>\n",
+       "      <th>Total Spent</th>\n",
+       "      <th>Payment Method</th>\n",
+       "      <th>Location</th>\n",
+       "      <th>Transaction Date</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>TXN_1961373</td>\n",
+       "      <td>Coffee</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>Credit Card</td>\n",
+       "      <td>Takeaway</td>\n",
+       "      <td>2023-09-08</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>TXN_4977031</td>\n",
+       "      <td>Cake</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>12.0</td>\n",
+       "      <td>Cash</td>\n",
+       "      <td>In-store</td>\n",
+       "      <td>2023-05-16</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>TXN_4271903</td>\n",
+       "      <td>Cookie</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>Credit Card</td>\n",
+       "      <td>In-store</td>\n",
+       "      <td>2023-07-19</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>TXN_7034554</td>\n",
+       "      <td>Salad</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>Digital Wallet</td>\n",
+       "      <td>Unknown</td>\n",
+       "      <td>2023-04-27</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>TXN_3160411</td>\n",
+       "      <td>Coffee</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>Digital Wallet</td>\n",
+       "      <td>In-store</td>\n",
+       "      <td>2023-06-11</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>TXN_2602893</td>\n",
+       "      <td>Smoothie</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>Credit Card</td>\n",
+       "      <td>Unknown</td>\n",
+       "      <td>2023-03-31</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>TXN_4433211</td>\n",
+       "      <td>Juice</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>Digital Wallet</td>\n",
+       "      <td>Takeaway</td>\n",
+       "      <td>2023-10-06</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>TXN_6699534</td>\n",
+       "      <td>Sandwich</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>16.0</td>\n",
+       "      <td>Cash</td>\n",
+       "      <td>Unknown</td>\n",
+       "      <td>2023-10-28</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>TXN_4717867</td>\n",
+       "      <td>Juice</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>Digital Wallet</td>\n",
+       "      <td>Takeaway</td>\n",
+       "      <td>2023-07-28</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>TXN_2064365</td>\n",
+       "      <td>Sandwich</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>Digital Wallet</td>\n",
+       "      <td>In-store</td>\n",
+       "      <td>2023-12-31</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  Transaction ID      Item  Quantity  Price Per Unit  Total Spent  \\\n",
+       "0    TXN_1961373    Coffee       2.0             2.0          4.0   \n",
+       "1    TXN_4977031      Cake       4.0             3.0         12.0   \n",
+       "2    TXN_4271903    Cookie       4.0             1.0          4.0   \n",
+       "3    TXN_7034554     Salad       2.0             5.0         10.0   \n",
+       "4    TXN_3160411    Coffee       2.0             2.0          4.0   \n",
+       "5    TXN_2602893  Smoothie       5.0             4.0         20.0   \n",
+       "6    TXN_4433211     Juice       3.0             3.0          9.0   \n",
+       "7    TXN_6699534  Sandwich       4.0             4.0         16.0   \n",
+       "8    TXN_4717867     Juice       5.0             3.0         15.0   \n",
+       "9    TXN_2064365  Sandwich       5.0             4.0         20.0   \n",
+       "\n",
+       "   Payment Method  Location Transaction Date  \n",
+       "0     Credit Card  Takeaway       2023-09-08  \n",
+       "1            Cash  In-store       2023-05-16  \n",
+       "2     Credit Card  In-store       2023-07-19  \n",
+       "3  Digital Wallet   Unknown       2023-04-27  \n",
+       "4  Digital Wallet  In-store       2023-06-11  \n",
+       "5     Credit Card   Unknown       2023-03-31  \n",
+       "6  Digital Wallet  Takeaway       2023-10-06  \n",
+       "7            Cash   Unknown       2023-10-28  \n",
+       "8  Digital Wallet  Takeaway       2023-07-28  \n",
+       "9  Digital Wallet  In-store       2023-12-31  "
+      ]
+     },
+     "execution_count": 57,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.head(10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Transaction ID</th>\n",
+       "      <th>Item</th>\n",
+       "      <th>Quantity</th>\n",
+       "      <th>Price Per Unit</th>\n",
+       "      <th>Total Spent</th>\n",
+       "      <th>Payment Method</th>\n",
+       "      <th>Location</th>\n",
+       "      <th>Transaction Date</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>9990</th>\n",
+       "      <td>TXN_1538510</td>\n",
+       "      <td>Coffee</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>Digital Wallet</td>\n",
+       "      <td>Unknown</td>\n",
+       "      <td>2023-05-22</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9991</th>\n",
+       "      <td>TXN_3897619</td>\n",
+       "      <td>Sandwich</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>12.0</td>\n",
+       "      <td>Cash</td>\n",
+       "      <td>Takeaway</td>\n",
+       "      <td>2023-02-24</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9992</th>\n",
+       "      <td>TXN_2739140</td>\n",
+       "      <td>Smoothie</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>16.0</td>\n",
+       "      <td>Digital Wallet</td>\n",
+       "      <td>In-store</td>\n",
+       "      <td>2023-07-05</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9993</th>\n",
+       "      <td>TXN_4766549</td>\n",
+       "      <td>Smoothie</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>Cash</td>\n",
+       "      <td>Unknown</td>\n",
+       "      <td>2023-10-20</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9994</th>\n",
+       "      <td>TXN_7851634</td>\n",
+       "      <td>Juice</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>16.0</td>\n",
+       "      <td>Digital Wallet</td>\n",
+       "      <td>Unknown</td>\n",
+       "      <td>2023-01-08</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9995</th>\n",
+       "      <td>TXN_7672686</td>\n",
+       "      <td>Coffee</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>Digital Wallet</td>\n",
+       "      <td>Unknown</td>\n",
+       "      <td>2023-08-30</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9996</th>\n",
+       "      <td>TXN_9659401</td>\n",
+       "      <td>Juice</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>Digital Wallet</td>\n",
+       "      <td>Unknown</td>\n",
+       "      <td>2023-06-02</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9997</th>\n",
+       "      <td>TXN_5255387</td>\n",
+       "      <td>Coffee</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>Digital Wallet</td>\n",
+       "      <td>Unknown</td>\n",
+       "      <td>2023-03-02</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9998</th>\n",
+       "      <td>TXN_7695629</td>\n",
+       "      <td>Cookie</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>Digital Wallet</td>\n",
+       "      <td>Unknown</td>\n",
+       "      <td>2023-12-02</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9999</th>\n",
+       "      <td>TXN_6170729</td>\n",
+       "      <td>Sandwich</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>12.0</td>\n",
+       "      <td>Cash</td>\n",
+       "      <td>In-store</td>\n",
+       "      <td>2023-11-07</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     Transaction ID      Item  Quantity  Price Per Unit  Total Spent  \\\n",
+       "9990    TXN_1538510    Coffee       5.0             2.0         10.0   \n",
+       "9991    TXN_3897619  Sandwich       3.0             4.0         12.0   \n",
+       "9992    TXN_2739140  Smoothie       4.0             4.0         16.0   \n",
+       "9993    TXN_4766549  Smoothie       2.0             4.0          8.0   \n",
+       "9994    TXN_7851634     Juice       4.0             4.0         16.0   \n",
+       "9995    TXN_7672686    Coffee       2.0             2.0          4.0   \n",
+       "9996    TXN_9659401     Juice       3.0             3.0          9.0   \n",
+       "9997    TXN_5255387    Coffee       4.0             2.0          8.0   \n",
+       "9998    TXN_7695629    Cookie       3.0             3.0          9.0   \n",
+       "9999    TXN_6170729  Sandwich       3.0             4.0         12.0   \n",
+       "\n",
+       "      Payment Method  Location Transaction Date  \n",
+       "9990  Digital Wallet   Unknown       2023-05-22  \n",
+       "9991            Cash  Takeaway       2023-02-24  \n",
+       "9992  Digital Wallet  In-store       2023-07-05  \n",
+       "9993            Cash   Unknown       2023-10-20  \n",
+       "9994  Digital Wallet   Unknown       2023-01-08  \n",
+       "9995  Digital Wallet   Unknown       2023-08-30  \n",
+       "9996  Digital Wallet   Unknown       2023-06-02  \n",
+       "9997  Digital Wallet   Unknown       2023-03-02  \n",
+       "9998  Digital Wallet   Unknown       2023-12-02  \n",
+       "9999            Cash  In-store       2023-11-07  "
+      ]
+     },
+     "execution_count": 58,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.tail(10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['Transaction ID',\n",
+       " 'Item',\n",
+       " 'Quantity',\n",
+       " 'Price Per Unit',\n",
+       " 'Total Spent',\n",
+       " 'Payment Method',\n",
+       " 'Location',\n",
+       " 'Transaction Date']"
+      ]
+     },
+     "execution_count": 59,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.columns.tolist()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df.to_csv(\"cleaned_cafe_sales.csv\", index=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "File exists: True\n"
+     ]
+    }
+   ],
+   "source": [
+    "import os\n",
+    "\n",
+    "print(\"File exists:\", os.path.exists(\"cleaned_cafe_sales.csv\"))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Confirmation to see whether our cleaned csv file is loading or not"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Shape: (9540, 8)\n",
+      "\n",
+      "Missing values:\n",
+      "Transaction ID      0\n",
+      "Item                0\n",
+      "Quantity            0\n",
+      "Price Per Unit      0\n",
+      "Total Spent         0\n",
+      "Payment Method      0\n",
+      "Location            0\n",
+      "Transaction Date    0\n",
+      "dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "cleaned_df = pd.read_csv(\"cleaned_cafe_sales.csv\")\n",
+    "\n",
+    "print(\"Shape:\", cleaned_df.shape)\n",
+    "print(\"\\nMissing values:\")\n",
+    "print(cleaned_df.isnull().sum())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "venv",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.14.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}